Research on attention-enhanced networks for subtype classification of age-related macular degeneration in optical coherence tomography.
10.7507/1001-5515.202408029
- Author:
Minghui CHEN
1
;
Wenyi YANG
1
;
Shiyi XU
1
;
Yanqi LU
1
;
Zhengqi YANG
1
;
Fugang LI
2
;
Zhensheng GU
3
Author Information
1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China.
2. Shanghai Upper Bio-Tech Pharma Co., LTD, Shanghai 200003, P. R. China.
3. Department of Ophthalmology, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, P. R. China.
- Publication Type:Journal Article
- Keywords:
Age-related macular degeneration;
Image classification;
Key point location;
Optical coherence tomography;
Two-dimensional relative self-attention mechanism
- MeSH:
Tomography, Optical Coherence/methods*;
Humans;
Macular Degeneration/diagnostic imaging*;
Neural Networks, Computer
- From:
Journal of Biomedical Engineering
2025;42(5):901-909
- CountryChina
- Language:Chinese
-
Abstract:
Subtype classification of age-related macular degeneration (AMD) based on optical coherence tomography (OCT) images serves as an effective auxiliary tool for clinicians in diagnosing disease progression and formulating treatment plans. To improve the classification accuracy of AMD subtypes, this study proposes a keypoint-based, attention-enhanced residual network (KPA-ResNet). The proposed architecture adopts a 50-layer residual network (ResNet-50) as the backbone, preceded by a keypoint localization module based on heatmap regression to outline critical lesion regions. A two-dimensional relative self-attention mechanism is incorporated into convolutional layers to enhance the representation of key lesion areas. Furthermore, the network depth is appropriately increased and an improved residual module, ConvNeXt, is introduced to enable comprehensive extraction of high-dimensional features and enrich the detail of lesion boundary contours, ultimately achieving higher classification accuracy of AMD subtypes. Experimental results demonstrate that KPA-ResNet achieves significant improvements in overall classification accuracy compared with conventional convolutional neural networks. Specifically, for the wet AMD subtypes, the classification accuracies for inactive choroidal neovascularization (CNV) and active CNV reach 92.8% and 95.2%, respectively, representing substantial improvement over ResNet-50. These findings validate the superior performance of KPA-ResNet in AMD subtype classification tasks. This work provides a high-accuracy, generalizable network architecture for OCT-based AMD subtype classification and offers new insights into integrating attention mechanisms with convolutional neural networks in ophthalmic image analysis.
